Analyzing the Recovery of the Canadian Economy Post COVID-19¶

In [1]:
from IPython.display import Image
Image(url='https://unsplash.com/photos/-ca8YHMKxFU/download?ixid=M3wxMjA3fDB8MXxhbGx8fHx8fHx8fHwxNjg5OTgwOTgyfA&force=true&w=1920')
Out[1]:

Objectives¶

This data analysis project provides a comprehensive exploration of the Canadian economy's recovery following the COVID-19 pandemic, from the initial impact in Q2 2020 through to Q2 2023. Utilizing an extensive dataset that encompasses a variety of industries, the analysis investigates the fluctuating economic health of each sector, highlighting areas of growth and decline.

Key areas of focus include the identification of industries that experienced the best and worst growth rates between Q2 2019 and Q2 2023. The study further compares these figures to performance metrics during the first wave of the pandemic in Q2 2020, providing a nuanced understanding of industry resilience and adaptability in the face of unprecedented challenges.

Furthermore, the project provides insights into the recovery journey of the industries that were most profoundly affected during the initial stages of the pandemic. Through an in-depth investigation of these sectors, this project seeks to evaluate whether they have been able to regain their footing and adapt to the 'new normal' precipitated by the COVID-19 crisis.

This portfolio project offers valuable insights for individuals looking to understand the economic ramifications of the COVID-19 pandemic, providing a thorough and detailed analysis of recovery trends across the diverse landscape of Canadian industries.

Required libraries¶

Let's start by importing the required libraries for this project:

  1. numpy
  2. pandas
  3. scipy
  4. statsmodels
  5. matplotlib
  6. seaborn
  7. plotly
In [2]:
import numpy as np
import pandas as pd
import scipy.stats as stats
from statsmodels.sandbox.stats.multicomp import multipletests
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px

%config InlineBackend.figure_format = 'retina'

Importing and cleaning a GDP dataset¶

📈 We will be using the GDP by industry dataset from Statistics Canada released on 2023-06-30: https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=3610043402

In [3]:
gdp = pd.read_csv('../data/36100434_gdp_by_industry_monthly.csv', usecols = ['REF_DATE', 'GEO', 'Seasonal adjustment', 'Prices', 'North American Industry Classification System (NAICS)', 'VALUE'])

🧐 We chose the following columns from this dataset:

1. REF_DATE: The month in which the data was collected;

2. GEO: The geographic region of the data. We are only interested in country-wide measurements (Canada);

3. Seasonal adjustment: The type of adjusment. Either "Seasonally adjusted at annual rates" or "Trading-day adjusted";

4. North American Industry Classification System (NAICS): The industry name and NAICS code from which the value was obtained from;

5. VALUE: The GDP measure in Canadian dollars (x1,000,000).

In [4]:
gdp.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 272076 entries, 0 to 272075
Data columns (total 6 columns):
 #   Column                                                 Non-Null Count   Dtype  
---  ------                                                 --------------   -----  
 0   REF_DATE                                               272076 non-null  object 
 1   GEO                                                    272076 non-null  object 
 2   Seasonal adjustment                                    272076 non-null  object 
 3   Prices                                                 272076 non-null  object 
 4   North American Industry Classification System (NAICS)  272076 non-null  object 
 5   VALUE                                                  237396 non-null  float64
dtypes: float64(1), object(5)
memory usage: 12.5+ MB

👉 We can optimize our dataset by changing the REF_DATE datatype to date/time and GEO, Seasonal adjustment, Prices, NAICS to category.

In [5]:
gdp['REF_DATE'] = pd.to_datetime(gdp['REF_DATE'])
gdp = gdp.astype({'GEO' : 'category',
          'Seasonal adjustment' : 'category',
          'Prices' : 'category', 
          'North American Industry Classification System (NAICS)': 'category'})
In [6]:
gdp.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 272076 entries, 0 to 272075
Data columns (total 6 columns):
 #   Column                                                 Non-Null Count   Dtype         
---  ------                                                 --------------   -----         
 0   REF_DATE                                               272076 non-null  datetime64[ns]
 1   GEO                                                    272076 non-null  category      
 2   Seasonal adjustment                                    272076 non-null  category      
 3   Prices                                                 272076 non-null  category      
 4   North American Industry Classification System (NAICS)  272076 non-null  category      
 5   VALUE                                                  237396 non-null  float64       
dtypes: category(4), datetime64[ns](1), float64(1)
memory usage: 5.5 MB

👉 We are mostly interested in long-term trends and thus we will use Seasonally adjusted prices constant prices (in millions of dollars)

In [7]:
gdp = gdp.query('`Seasonal adjustment` == "Seasonally adjusted at annual rates" and Prices == "2012 constant prices"').copy()
In [8]:
gdp
Out[8]:
REF_DATE GEO Seasonal adjustment Prices North American Industry Classification System (NAICS) VALUE
287 1997-01-01 Canada Seasonally adjusted at annual rates 2012 constant prices All industries [T001] 1161269.0
288 1997-01-01 Canada Seasonally adjusted at annual rates 2012 constant prices Goods-producing industries [T002] 402792.0
289 1997-01-01 Canada Seasonally adjusted at annual rates 2012 constant prices Service-producing industries [T003] 756654.0
290 1997-01-01 Canada Seasonally adjusted at annual rates 2012 constant prices Business sector industries [T004] NaN
291 1997-01-01 Canada Seasonally adjusted at annual rates 2012 constant prices Business sector, goods [T005] NaN
... ... ... ... ... ... ...
271784 2023-04-01 Canada Seasonally adjusted at annual rates 2012 constant prices Defence services [9111] 12660.0
271785 2023-04-01 Canada Seasonally adjusted at annual rates 2012 constant prices Federal government public administration (exce... 43931.0
271786 2023-04-01 Canada Seasonally adjusted at annual rates 2012 constant prices Provincial and territorial public administrati... 37278.0
271787 2023-04-01 Canada Seasonally adjusted at annual rates 2012 constant prices Local, municipal and regional public administr... 46027.0
271788 2023-04-01 Canada Seasonally adjusted at annual rates 2012 constant prices Aboriginal public administration [914] 5230.0

90692 rows × 6 columns

✅ Let's also rename a couple of columns to make our life easier:

  1. 'REF_DATE' will become 'date';
  2. 'North American Industry Classification System (NAICS)' will become 'industry';
  3. 'VALUE' will become 'value'.
In [9]:
gdp = gdp.rename(columns = {'REF_DATE' : 'date',
                          'North American Industry Classification System (NAICS)': 'industry',
                          'VALUE' : 'value'}).set_index('date')[['industry', 'value']].copy()
gdp
Out[9]:
industry value
date
1997-01-01 All industries [T001] 1161269.0
1997-01-01 Goods-producing industries [T002] 402792.0
1997-01-01 Service-producing industries [T003] 756654.0
1997-01-01 Business sector industries [T004] NaN
1997-01-01 Business sector, goods [T005] NaN
... ... ...
2023-04-01 Defence services [9111] 12660.0
2023-04-01 Federal government public administration (exce... 43931.0
2023-04-01 Provincial and territorial public administrati... 37278.0
2023-04-01 Local, municipal and regional public administr... 46027.0
2023-04-01 Aboriginal public administration [914] 5230.0

90692 rows × 2 columns

👉 Let's separate the industry code from the industry name and assign it to a new column named 'industry_code'.

In [10]:
new_df = gdp['industry'].str.split('[', expand=True)
gdp['industry'] = new_df[0].str.strip()
gdp['industry_code'] = new_df[1].str.replace(']', '').str.strip()
gdp = gdp[['industry', 'industry_code', 'value']]
gdp
Out[10]:
industry industry_code value
date
1997-01-01 All industries T001 1161269.0
1997-01-01 Goods-producing industries T002 402792.0
1997-01-01 Service-producing industries T003 756654.0
1997-01-01 Business sector industries T004 NaN
1997-01-01 Business sector, goods T005 NaN
... ... ... ...
2023-04-01 Defence services 9111 12660.0
2023-04-01 Federal government public administration (exce... 911A 43931.0
2023-04-01 Provincial and territorial public administration 912 37278.0
2023-04-01 Local, municipal and regional public administr... 913 46027.0
2023-04-01 Aboriginal public administration 914 5230.0

90692 rows × 3 columns

‼️ Cannabis has been legalized in Canada in 2018 and thus will skew these results. Let's remove them from the analysis!

In [11]:
gdp = gdp[~gdp['industry'].str.contains('|'.join(['Cannabis', 'cannabis']))].copy()

👉 Great, we can start taking a look at some insights from this dataset.

💰 Let's use seaborn to plot the overall growth of the Canadian economy across all industries.

In [12]:
# Create a line plot using Seaborn
sns.lineplot(data = gdp.query('industry == "All industries"'), x = 'date', y = 'value')

# Highlight a section using axvspan() or axhspan()
plt.axvspan('2020-01-01', '2022-01-01', color='red', alpha=0.2)

# Set labels and title
plt.xlabel('Date')
plt.ylabel('GDP by industry in CAD dollars (x 1,000,000)')

# Show the plot
plt.show()

☝️ We can clearly observe the impact of the COVID-19 pandemic on the Canadian economy in the chart above.

🧐 It does seem that the overall economy has somewhat recovered since 2020, but is this true for all industries? Let's take a look!

👉 Let's first aggregate our data by quarterly periods:

In [13]:
gdp_grouped = gdp.groupby([gdp.index.to_period('Q'), 'industry', 'industry_code']).median()
gdp_grouped
Out[13]:
value
date industry industry_code
1997Q1 Aboriginal public administration 914 NaN
Accommodation and food services 72 28186.0
Accommodation services 721 9371.0
Accounting, tax preparation, bookkeeping and payroll services 5412 NaN
Activities related to credit intermediation 5223 NaN
... ... ... ...
2023Q2 Water, sewage and other systems 2213 3375.0
Wholesale electronic markets, and agents and brokers 419 3107.0
Wholesale trade 41 106337.0
Wineries and distilleries 3121A 1099.0
Wood product manufacturing 321 9149.0

28938 rows × 1 columns

👌 Let's also specify the date range in which we will be analyzing our data. I chose 2019Q2 as the baseline for two reasons:

  1. It controls for seasonality by looking at a year before the peak of the COVID-19 pandemic (2020Q2);
  2. Our latest data is from 2023Q2, allowing us to have a full 4-year outlook at the economy.
In [14]:
date_range = ['2019Q2', '2023Q2']

👇 We will use a function to filter the dataset between the time ranges above and eliminate any time periods and industries with NA's:

In [15]:
def filter_datarange(df, date_range, index_columns, date_column, value_column):
    """
    This function will filter the dataset between specific time periods and eliminate any time periods and industries with NA's.
    It will then calculate the cumulative growth since the first period in the date_range

    Parameters
    ----------
    df : Panda's DataFrame
        DataFrame to apply function
    date_range : list or tupple
        Two elements, first and last periods to analyze
    index_columns : list or tupple
        Column names to create index from
    date_column : str
        Column name containing dates
    value_column: str
        Column name containing values
        
    Returns
    -------
    Panda's DataFrame
    """
    df = df.reset_index()
    df_pivot = df[(df[date_column] >= date_range[0]) & (df[date_column] <= date_range[1])].pivot(index = index_columns, columns = date_column, values = value_column).dropna(how = 'any')
    df_melt = df_pivot.reset_index().melt(id_vars = index_columns, var_name = date_column, value_name = value_column).set_index(date_column).sort_index()
    df_melt['cumulative_growth'] = df_melt.groupby(index_columns).transform(lambda x: (x - x.iloc[0]) / x.iloc[0] * 100)
    return df_melt
In [16]:
gdp_clean = filter_datarange(df = gdp_grouped, date_range = date_range, index_columns = ['industry', 'industry_code'], date_column = 'date', value_column = 'value')
gdp_clean
Out[16]:
industry industry_code value cumulative_growth
date
2019Q2 Aboriginal public administration 914 4517.0 0.000000
2019Q2 Accommodation and food services 72 43887.0 0.000000
2019Q2 Accommodation services 721 13599.0 0.000000
2019Q2 Accounting, tax preparation, bookkeeping and p... 5412 12954.0 0.000000
2019Q2 Activities related to credit intermediation 5223 3187.0 0.000000
... ... ... ... ...
2023Q2 Water, sewage and other systems 2213 3375.0 3.147922
2023Q2 Wholesale electronic markets, and agents and b... 419 3107.0 2.541254
2023Q2 Wholesale trade 41 106337.0 1.487908
2023Q2 Wineries and distilleries 3121A 1099.0 15.320042
2023Q2 Wood product manufacturing 321 9149.0 2.820859

4641 rows × 4 columns

Great, this dataset is now ready to be analyzed!

How has the COVID-19 pandemic affected each Canadian industry?¶

What are the 10 industries with the highest GDP gain since 2019Q2?¶

👇 Let's create a function to visualize the cumulative growth of specific industries using plotly:

In [17]:
def plot_line_graph(df, industry_codes, y_col, title, y_axis_title):
    """
    This function will plot a line graph based on the list of industry codes provided and name it according to title.

    Parameters
    ----------
    df : Panda's DataFrame
        DataFrame to plot data from
    industry_codes : list or tupple
        List of industry codes to filter df
    y_col: str
        Column to take the y-axis values from
    title : str
        Plot title
        
    Returns
    -------
    plotly plot
    """
    industry_codes = industry_codes
    df.index = df.index.astype('str')
    fig = px.line(df.query('industry_code in @industry_codes'), y=y_col, color='industry', markers=True, color_discrete_sequence=px.colors.qualitative.Dark24, template='plotly_white')
    # Modify the layout to change the axis labels
    fig.update_layout(
        title_text=title,
        xaxis_title='Year',  # Set the x-axis label
        yaxis_title=' '.join([y_axis_title, 'since', date_range[0]]),
        shapes=[
        dict(
            type='line',
            xref='x',
            yref='y',
            x0=date_range[0],
            y0=0,
            x1=date_range[1],
            y1=0,
            line=dict(
                color='black',
                width=2,
                dash='dash'
            )
        )
    ]
    )
    fig.show()

🧐 Now let's filter our dataset to find out which industries had the highest cumulative growth since 2019Q2.

In [18]:
best_industries_gdp = gdp_clean.xs(date_range[1]).nlargest(n = 10, columns = 'cumulative_growth')
best_industries_gdp_list = best_industries_gdp['industry_code'].tolist()
best_industries_gdp
Out[18]:
industry industry_code value cumulative_growth
date
2023Q2 Data processing, hosting, and related services 518 6775.0 64.641555
2023Q2 Non-store retailers 454 6290.0 50.514477
2023Q2 Tobacco manufacturing 3122 1354.0 43.736730
2023Q2 Computer systems design and related services 5415 45908.0 37.944712
2023Q2 Computer and peripheral equipment manufacturing 3341 259.0 35.602094
2023Q2 Industrial machinery manufacturing 3332 2800.0 35.200386
2023Q2 Engine, turbine and power transmission equipme... 3336 677.0 31.456311
2023Q2 Warehousing and storage 493 4998.0 28.351310
2023Q2 Pesticide, fertilizer and other agricultural c... 3253 2915.0 28.244611
2023Q2 Ventilation, heating, air-conditioning and com... 3334 2453.0 26.248070
In [19]:
plot_line_graph(df = gdp_clean, industry_codes = best_industries_gdp_list, y_col = 'cumulative_growth', title = ''.join(['Industries with the highest cumulative growth since ', date_range[0]]), y_axis_title = 'Cumulative growth (%)')

🧐 It looks like "Data processing, hosting, and related services" has been the industry with the highest growth in Canada since 2019Q2!

👇 In fact, we can visualize how massive this growth has been when compared to the industry average below:

In [20]:
def plot_line_industries(df, industry_names):
    """
    This function will plot a line graph based on the list of industry names provided.

    Parameters
    ----------
    df : Panda's DataFrame
        DataFrame to plot data from
    industry_names : list or tupple
        List of industry names to filter df
        
    Returns
    -------
    seaborn plot
    """
    sns.relplot(data = df.query('industry in @industry_names'), x = 'date', y = 'cumulative_growth', hue = 'industry', kind = 'line')
    plt.xticks(df.index[gdp_clean.index.str.contains('Q2')].unique().to_list())

    # Set labels and title
    plt.xlabel('Date')
    plt.ylabel('Cumulative growth (%) since '+date_range[0])

    #Set threshold
    plt.axhline(0, color='black', linestyle='--')

    # Show the plot
    plt.show()
In [21]:
plot_line_industries(df = gdp_clean, industry_names = ["All industries", "Data processing, hosting, and related services"])

What are the 10 industries with the lowest GDP gain since 2019Q2?¶

👉 Let's repeat the analysis and look at the industries with the lowest growth since 2019Q2:

In [22]:
worst_industries_gdp = gdp_clean.xs(date_range[1]).nsmallest(n = 10, columns = 'cumulative_growth')
worst_industries_gdp_list = worst_industries_gdp['industry_code'].tolist()
worst_industries_gdp
Out[22]:
industry industry_code value cumulative_growth
date
2023Q2 Management of companies and enterprises 55 1504.0 -72.993356
2023Q2 Other non-metallic mineral mining and quarryin... 21239X 2071.0 -35.981453
2023Q2 Non-ferrous metal (except aluminum) production... 3314 2232.0 -35.862069
2023Q2 Electric lighting equipment manufacturing 3351 488.0 -32.782369
2023Q2 Automotive equipment rental and leasing 5321 2974.0 -31.221092
2023Q2 Printing and related support activities 323 3289.0 -31.134841
2023Q2 Copper, nickel, lead and zinc ore mining 21223 4571.0 -29.892638
2023Q2 Other furniture-related product manufacturing 3379 258.0 -29.508197
2023Q2 Motor vehicle manufacturing 3361 4961.0 -27.449547
2023Q2 Hardware manufacturing 3325 522.0 -27.094972
In [23]:
plot_line_graph(df = gdp_clean, industry_codes = worst_industries_gdp_list, y_col = 'cumulative_growth', title = ''.join(['Industries with the lowest cumulative growth since ', date_range[0]]), y_axis_title = 'Cumulative growth (%)')

🧐 It looks like "Management of companies and enterprises" has been the industry with the lowest growth in Canada since 2019Q2.

👇 In fact, this industry has been steadily declining since at least 2019Q2!

In [24]:
plot_line_industries(df = gdp_clean, industry_names = ["All industries", "Management of companies and enterprises"])

👇 Interestingly, if we look at historical data we can see that the "Data processing, hosting, and related services" industry started gaining a lot of momentum at a similar time when "Management of companies and enterprises" started to decline.

🧐 This may represent a shift in the Canadian economy to a more tech and data-focused around 2015.

In [25]:
data_processing = gdp_grouped.xs('Data processing, hosting, and related services', level=1).dropna()
management = gdp_grouped.xs('Management of companies and enterprises', level = 1).dropna()
data_processing_management = pd.concat([data_processing, management], keys = ['Data processing, hosting, and related services', 'Management of companies and enterprises']).reset_index(names = ['industry', 'date', 'industry_code'])
data_processing_management['date'] = data_processing_management['date'].astype('str')
sns.relplot(data_processing_management, x = 'date', y = 'value', kind = 'line', hue = 'industry')
plt.xticks(data_processing_management[data_processing_management['date'].str.contains('Q2')]['date'].unique().tolist())
plt.xticks(rotation=90)
plt.axvline('2015Q2', color='black', linestyle='--')
# Set labels and title
plt.xlabel('Date')
plt.ylabel('GDP value per industry in CAD dollars (x 1,000,000)')
plt.show()

What were the industries with the best and worst performance during the firts wave of the COVID-19 pandemic (2020Q2)?¶

👉 The COVID-19 pandemic hit the economy the hardest in 2020Q2. What were the industries that had the best and worst performance during this period of time?

📈 Let's create a function to visualize the types of industries most affected during the pandemic:

In [26]:
def plot_bar_plot(df, title, xaxis_title):
    """
    This function will plot a bar graph based on the dataframe provided.

    Parameters
    ----------
    df : Panda's DataFrame
        DataFrame to plot data from
    title : str
        Plot title
        
    Returns
    -------
    plotly plot
    """
    fig = px.bar(df, x = 'cumulative_growth', y = 'industry', color='industry', color_discrete_sequence=px.colors.qualitative.Dark24, template='plotly_white')
    fig.update_layout(
    showlegend=False,
    title_text=title,
    yaxis_title='Industry',  # Set the x-axis label
    xaxis_title=xaxis_title)
    fig.show()
In [27]:
best_industries_gdp_2020 = gdp_clean.xs('2020Q2').nlargest(n = 10, columns = 'cumulative_growth')
best_industries_gdp_2020
Out[27]:
industry industry_code value cumulative_growth
date
2020Q2 Non-store retailers 454 5748.0 37.544867
2020Q2 Wineries and distilleries 3121A 1237.0 29.800630
2020Q2 Converted paper product manufacturing 3222 3200.0 12.478032
2020Q2 Couriers and messengers 492 3837.0 10.960093
2020Q2 Iron ore mining 21221 7541.0 9.084334
2020Q2 Banking, monetary authorities and other deposi... 52BX 71491.0 8.726598
2020Q2 Depository credit intermediation and monetary ... 52B 76358.0 8.387628
2020Q2 Postal service and couriers and messengers 49A 7387.0 8.091893
2020Q2 Data processing, hosting, and related services 518 4435.0 7.776428
2020Q2 Crop production 111 30310.0 6.721594
In [28]:
plot_bar_plot(df = best_industries_gdp_2020, title = 'Industries with the best performance during the first wave of the COVID-19 pandemic (2020Q2)', xaxis_title = 'GDP growth (%) in 2020Q2')

☝️ Not surprisingly, the best performing industry during the height of the COVID-19 pandemic was "Non-store retailers", which include online shops.

In [29]:
worst_industries_gdp_2020 = gdp_clean.xs('2020Q2').nsmallest(n = 10, columns = 'cumulative_growth')
worst_industries_gdp_2020
Out[29]:
industry industry_code value cumulative_growth
date
2020Q2 Air transportation 481 216.0 -97.691074
2020Q2 Urban transit systems 4851 813.0 -86.508463
2020Q2 Motor vehicle manufacturing 3361 1342.0 -80.374378
2020Q2 Motor vehicles and parts manufacturing 336Y 4808.0 -72.183975
2020Q2 Motor vehicle parts manufacturing 3363 2732.0 -70.836892
2020Q2 Accommodation services 721 4134.0 -69.600706
2020Q2 Transit, ground passenger, scenic and sightsee... 48Z 3100.0 -67.495019
2020Q2 Hardware manufacturing 3325 241.0 -66.340782
2020Q2 Gambling industries 7132 1061.0 -62.429178
2020Q2 Performing arts, spectator sports and related ... 71A 2973.0 -61.855273
In [30]:
plot_bar_plot(df = worst_industries_gdp_2020, title = 'Industries with the worst performance during the firts wave of the COVID-19 pandemic (2020Q2)', xaxis_title = 'GDP growth (%) in 2020Q2')

☝️ Also not surprisingly, the worst performing industries during 2020Q2 were related to transporation, travel, and leasure.

How have these industries performed since 2020Q2?¶

👉 A lot of industries have struggled to recover from the COVID-19 pandemic, whereas others that initially performed well have declined since the tech boom in 2020.

👇 Let's take a look at how the industries with the best and worst performance in 2020 have performed since the end of the COVID-19 pandemic:

In [31]:
best_and_worst_industries_gdp_2020 = pd.concat([best_industries_gdp_2020, worst_industries_gdp_2020], keys = ['best', 'worst']).reset_index(names=['category', 'date'])[['industry', 'industry_code', 'category']]
best_and_worst_industries_gdp = gdp_clean.reset_index(names='date').merge(best_and_worst_industries_gdp_2020, how='inner', on=['industry', 'industry_code']).sort_values(['date', 'category'])
#best_and_worst_industries_gdp = best_and_worst_industries_gdp.groupby(['category', 'date'])['cumulative_growth'].aggregate(['mean', 'std'])
best_and_worst_industries_gdp
Out[31]:
date industry industry_code value cumulative_growth category
34 2019Q2 Banking, monetary authorities and other deposi... 52BX 65753.0 0.000000 best
51 2019Q2 Converted paper product manufacturing 3222 2845.0 0.000000 best
68 2019Q2 Couriers and messengers 492 3458.0 0.000000 best
85 2019Q2 Crop production 111 28401.0 0.000000 best
102 2019Q2 Data processing, hosting, and related services 518 4115.0 0.000000 best
... ... ... ... ... ... ...
220 2023Q2 Motor vehicle parts manufacturing 3363 10474.0 11.806149 worst
237 2023Q2 Motor vehicles and parts manufacturing 336Y 16689.0 -3.448076 worst
271 2023Q2 Performing arts, spectator sports and related ... 71A 6417.0 -17.667436 worst
305 2023Q2 Transit, ground passenger, scenic and sightsee... 48Z 7426.0 -22.134843 worst
322 2023Q2 Urban transit systems 4851 4404.0 -26.916694 worst

340 rows × 6 columns

In [32]:
best_and_worst_industries_gdp_2020_list = best_and_worst_industries_gdp_2020['industry_code'].tolist()
plot_line_graph(df = gdp_clean, industry_codes = best_and_worst_industries_gdp_2020_list, y_col = 'cumulative_growth', title = 'Industries with the lowest cumulative growth in 2020Q2', y_axis_title = 'Cumulative growth (%)')

Overall we can see the following trends:

  1. Both "Data processing, hosting, and related services", and "Non-store retailers" have managed to maintain a good performance since 2019Q2;
  2. Some industries such as "Couries and messengers, and "Converted paper product manufacturing" have diminished their growth after a surge during the COVID-19 pandemic;
  3. Manufacturing has recovered quite well since the big losses during 2020Q2;
  4. Urban transit is still struggling to regain growth since 2020Q2.

Does the growth of industries with the best/worst performance in 2020Q2 still differ in 2023?¶

🧐 One way to test if the growth of the industries impacted in 2020 has recovered after the end of the COVID-19 pandemic is to run a statistical comparison between each category and the average across all industries in each time point.

👉 We have two hypothesis: either the industries show a similar cumulative growth to the average across all industries (null-hypothesis) or they show a significantly different cumulative growth (alternative hypothesis) over time.

✅ First we have to prepare our dataset. We have to separate industries into three categories: those that performed the best, the worst, and the average across industries in 2020Q2.

In [33]:
#The first quarter in our comparison, which is 2020Q2
start_quarter = pd.PeriodIndex([date_range[0]], freq='Q').to_timestamp()

#Gather industries that are not within the best nor the worst datasets
cat_industries_gdp = gdp_clean.query('industry not in @best_and_worst_industries_gdp["industry"]').query('industry not in ["All industries"]').reset_index('date')
cat_industries_gdp['category'] = 'average'

#Then concatenate the average, best, and worst industries together
cat_industries_gdp = pd.concat([cat_industries_gdp, best_and_worst_industries_gdp]).reset_index(drop = True)
cat_industries_gdp['category'] = cat_industries_gdp['category'].astype('category')
cat_industries_gdp
Out[33]:
date industry industry_code value cumulative_growth category
0 2019Q2 Aboriginal public administration 914 4517.0 0.000000 average
1 2019Q2 Accommodation and food services 72 43887.0 0.000000 average
2 2019Q2 Accounting, tax preparation, bookkeeping and p... 5412 12954.0 0.000000 average
3 2019Q2 Activities related to credit intermediation 5223 3187.0 0.000000 average
4 2019Q2 Administrative and support services 561 45215.0 0.000000 average
... ... ... ... ... ... ...
4619 2023Q2 Motor vehicle parts manufacturing 3363 10474.0 11.806149 worst
4620 2023Q2 Motor vehicles and parts manufacturing 336Y 16689.0 -3.448076 worst
4621 2023Q2 Performing arts, spectator sports and related ... 71A 6417.0 -17.667436 worst
4622 2023Q2 Transit, ground passenger, scenic and sightsee... 48Z 7426.0 -22.134843 worst
4623 2023Q2 Urban transit systems 4851 4404.0 -26.916694 worst

4624 rows × 6 columns

👇 Let's run multiple t-tests and verify if our hypothesis is rejected (p<0.05):

In [34]:
### Function to define significance level
def p_value_signif(x):
    if x < 0.001:
        return '***'
    elif x < 0.01:
        return '**'
    elif x < 0.05:
        return '*'
    else:
        return 'n.s'
    

### Function to calculate t-test
def t_test_quarters(df, date_column, group_column, groups, control, value_column):
    
    # Initialize lists to store values
    p_values = []
    comparison = []
    quarters_list = []
    
    # Unique quarters
    quarters = df[date_column].unique()
    
    # Length of comparison groups
    len_groups = len(groups)

    # Perform t-test for each quarter
    for quarter in quarters:
        df_quarter = df[df[date_column] == quarter]
        
        # Perform comparison between each group and control
        for i in range(len_groups):
            t_stat, p_value = stats.ttest_ind(df_quarter[df_quarter[group_column] == groups[i]][value_column], 
                                              df_quarter[df_quarter[group_column] == control][value_column], equal_var=False)
            p_values.append(p_value)
            quarters_list.append(quarter)
            comparison.append('-'.join([groups[i], control]))
        
    # Perform multiple test correction
    reject, pvals_corrected, _, _ = multipletests(p_values, method='holm')

    # Create DataFrame with quarters, comparison, and p-values
    quarters = quarters * 3
    p_values_df = pd.DataFrame({
        'quarter': quarters_list,
        'comparison' : comparison,
        'p_value': pvals_corrected
    })
    
    # Apply significance level function
    p_values_df['p_value_signif'] = p_values_df['p_value'].apply(p_value_signif)
    return p_values_df
In [35]:
cat_industries_gdp_ttest = t_test_quarters(df=cat_industries_gdp, date_column='date', group_column='category', groups = ['best', 'worst'], control = 'average', value_column='cumulative_growth')
cat_industries_gdp_ttest
Out[35]:
quarter comparison p_value p_value_signif
0 2019Q2 best-average NaN n.s
1 2019Q2 worst-average NaN n.s
2 2019Q3 best-average 2.917339e-01 n.s
3 2019Q3 worst-average 1.000000e+00 n.s
4 2019Q4 best-average 1.638482e-01 n.s
5 2019Q4 worst-average 1.000000e+00 n.s
6 2020Q1 best-average 4.358711e-02 *
7 2020Q1 worst-average 7.997261e-01 n.s
8 2020Q2 best-average 1.327263e-04 ***
9 2020Q2 worst-average 7.713381e-07 ***
10 2020Q3 best-average 8.187044e-03 **
11 2020Q3 worst-average 6.260518e-02 n.s
12 2020Q4 best-average 3.447360e-03 **
13 2020Q4 worst-average 3.667422e-02 *
14 2021Q1 best-average 1.063311e-02 *
15 2021Q1 worst-average 1.055122e-02 *
16 2021Q2 best-average 4.755808e-02 *
17 2021Q2 worst-average 2.547329e-03 **
18 2021Q3 best-average 6.260518e-02 n.s
19 2021Q3 worst-average 2.770224e-03 **
20 2021Q4 best-average 6.260518e-02 n.s
21 2021Q4 worst-average 3.976040e-03 **
22 2022Q1 best-average 6.260518e-02 n.s
23 2022Q1 worst-average 2.505858e-03 **
24 2022Q2 best-average 6.260518e-02 n.s
25 2022Q2 worst-average 7.867950e-03 **
26 2022Q3 best-average 8.845680e-02 n.s
27 2022Q3 worst-average 5.509357e-03 **
28 2022Q4 best-average 6.829864e-02 n.s
29 2022Q4 worst-average 1.164943e-02 *
30 2023Q1 best-average 1.528044e-01 n.s
31 2023Q1 worst-average 3.619622e-02 *
32 2023Q2 best-average 1.836634e-01 n.s
33 2023Q2 worst-average 1.202744e-01 n.s

👇 Great, let's visualize how the cumulative growth of each group has progressed over time and how they deviate from the industry average:

In [36]:
sns.lineplot(data = cat_industries_gdp, x = 'date', y = 'cumulative_growth', hue = 'category', errorbar='sd')
plt.xticks(rotation=90)
plt.title('Recovery of industries based on their performace in 2020Q2')
plt.axhline(0, color='black', linestyle='--')
## Add p-value at each timepoint. 
##Green represents the best industries vs. average. Orange represents the worst industries vs. average.
for i in range(len(cat_industries_gdp_ttest['quarter'].unique())):
    plt.text(cat_industries_gdp_ttest.query('comparison == "best-average"')['quarter'].iloc[i], 50, cat_industries_gdp_ttest.query('comparison == "best-average"')['p_value_signif'].iloc[i], rotation=90, color = 'darkorange', horizontalalignment = 'center')
    plt.text(cat_industries_gdp_ttest.query('comparison == "worst-average"')['quarter'].iloc[i], -95, cat_industries_gdp_ttest.query('comparison == "worst-average"')['p_value_signif'].iloc[i], rotation=90, color = 'darkgreen', horizontalalignment = 'center')
plt.legend(loc = 'upper left')
plt.xlabel('Date')
plt.ylabel('Cumulative growth since '+date_range[0]+' (%)')
plt.ylim(-100, 100)
plt.show()

✅ We can clearly see that, after 2020Q2, the industries in the best/worst performance have significantly deviated from the industry average, as expected.

🧐 Interestingly, whereas the growth of the best performing industries have slowly fallen back to the industry average, the growth of the worst performing industries were still struggling until early 2023.

👉 This indicates that the industries most affected by the COVID-19 pandemic have not been able to fully recover just yet, although the trend seems to be getting better in 2023.

Importing and cleaning an employment dataset¶

📈 To verify how employment patterns have changed in the last couple of years, we will be using the employment and weekly income by industry dataset from Statistics Canada released on 2023-06-29: https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=1410022002

In [37]:
employment_earnings = pd.read_csv('/Users/dudi/Dropbox/UofToronto/Project/Portfolio/covid_recovery/data/14100220_employment_earnings_by_industry_weekly.csv', usecols = ['REF_DATE', 'Estimate', 'North American Industry Classification System (NAICS)', 'VALUE'])

🧐 We chose the following columns from this dataset:

1. REF_DATE: The month in which the data was collected;

2. Estimate: The type of data, either "Employment for all employees" or "Average weekly earnings including overtime for all employees".

3. North American Industry Classification System (NAICS): The industry name and NAICS code from which the value was obtained from;

4. VALUE: The GDP measure in Canadian dollars (x1,000,000).

In [38]:
employment_earnings.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 231904 entries, 0 to 231903
Data columns (total 4 columns):
 #   Column                                                 Non-Null Count   Dtype  
---  ------                                                 --------------   -----  
 0   REF_DATE                                               231904 non-null  object 
 1   Estimate                                               231904 non-null  object 
 2   North American Industry Classification System (NAICS)  231904 non-null  object 
 3   VALUE                                                  214765 non-null  float64
dtypes: float64(1), object(3)
memory usage: 7.1+ MB

👉 Once again, we can optimize our dataset by changing the REF_DATE datatype to date/time and NAICS to category.

In [39]:
employment_earnings['REF_DATE'] = pd.to_datetime(employment_earnings['REF_DATE'])
employment_earnings = employment_earnings.astype({'Estimate' : 'category',
          'North American Industry Classification System (NAICS)': 'category'})
In [40]:
employment_earnings.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 231904 entries, 0 to 231903
Data columns (total 4 columns):
 #   Column                                                 Non-Null Count   Dtype         
---  ------                                                 --------------   -----         
 0   REF_DATE                                               231904 non-null  datetime64[ns]
 1   Estimate                                               231904 non-null  category      
 2   North American Industry Classification System (NAICS)  231904 non-null  category      
 3   VALUE                                                  214765 non-null  float64       
dtypes: category(2), datetime64[ns](1), float64(1)
memory usage: 4.2 MB

👉 Similarly to what we have done before, let's rename our columns:

  1. 'REF_DATE' to 'date';
  2. 'North American Industry Classification System (NAICS)' to 'industry'
  3. 'VALUE' to 'value'
  4. 'Estimate' to 'estimate'

👉 Let's also separate the industry code from the industry name and assign it to 'industry_code'.

In [41]:
employment_earnings = employment_earnings.rename(columns = {'REF_DATE' : 'date',
                          'North American Industry Classification System (NAICS)': 'industry',
                          'VALUE' : 'value', 
                            'Estimate' : 'estimate'}).set_index('date')[['industry', 'estimate', 'value']].copy()

new_df = employment_earnings['industry'].str.split('[', expand=True)
employment_earnings['industry'] = new_df[0].str.strip()
employment_earnings['industry_code'] = new_df[1].str.replace(']', '').str.strip()
employment_earnings = employment_earnings[['industry', 'industry_code', 'estimate', 'value']]
employment_earnings = employment_earnings[~employment_earnings['industry'].str.contains('|'.join(['Cannabis', 'cannabis']))].copy()
In [42]:
employment_earnings['estimate'].unique()
Out[42]:
['Employment for all employees', 'Average weekly earnings including overtime fo...]
Categories (2, object): ['Average weekly earnings including overtime fo..., 'Employment for all employees']

We have two types of data in this dataset:

  1. Employment number for all employess per each industry

  2. Average weekly earning (including overtime) per each industry

✅ Let's separate them into two distinct datasets: employment_df and income_df

In [43]:
employment_df = employment_earnings.query('estimate == "Employment for all employees"').drop('estimate', axis=1)
income_df = employment_earnings.query('estimate == "Average weekly earnings including overtime for all employees"').drop('estimate', axis=1)
In [44]:
employment_df
Out[44]:
industry industry_code value
date
2001-01-01 Industrial aggregate including unclassified bu... 00-91N 12914390.0
2001-01-01 Industrial aggregate excluding unclassified bu... 11-91N 12835195.0
2001-01-01 Goods producing industries 11-33N 2910904.0
2001-01-01 Forestry, logging and support 11N 70073.0
2001-01-01 Forestry and logging 113 50650.0
... ... ... ...
2023-04-01 Federal government public administration 911 262969.0
2023-04-01 Provincial and territorial public administration 912 340516.0
2023-04-01 Local, municipal and regional public administr... 913 482325.0
2023-04-01 Indigenous public administration 914 69496.0
2023-04-01 Unclassified businesses 00 338373.0

115416 rows × 3 columns

Employment data analysis¶

📈 Let's take an overall look at the historical employment data from early 2000's to 2023

In [45]:
# Create a line plot using Seaborn
sns.lineplot(data = employment_df.query('industry == "Industrial aggregate excluding unclassified businesses"'), x = 'date', y = 'value')

# Highlight a section using axvspan() or axhspan()
plt.axvspan('2020-01-01', '2022-01-01', color='red', alpha=0.2)

# Set labels and title
plt.xlabel('Date')
plt.ylabel('Employment for all industries (x 10,000,000)')

# Show the plot
plt.show()

☝️ We can again observe the impact of the COVID-19 pandemic on the employment numbers in the chart above.

🧐 How has employment numbers recovered for different industries since the end of the pandemic?

👉 Let's first aggregate our data by quarterly periods:

In [46]:
employment_grouped = employment_df.groupby([employment_df.index.to_period('Q'), 'industry', 'industry_code']).median()
employment_grouped
Out[46]:
value
date industry industry_code
2001Q1 Accommodation and food services 72 947156.0
Accommodation services 721 186626.0
Accounting, tax preparation, bookkeeping and payroll services 5412 65452.0
Activities related to credit intermediation 5223 12161.0
Activities related to real estate 5313 35544.0
... ... ... ...
2023Q2 Web search portals, libraries and archives, and all other information services 5192 26583.0
Web search portals, libraries, archives, and all other information services 519 26583.0
Wholesale trade 41 820614.0
Wired and wireless telecommunications carriers (except satellite) 5173 100164.0
Wood product manufacturing 321 94213.0

38736 rows × 1 columns

👇 We can use the filter_datarange function from the previous section to filter the dataset and eliminate any time periods and industries with NA's

In [47]:
employment_clean = filter_datarange(df = employment_grouped, date_range = date_range, index_columns = ['industry', 'industry_code'], date_column = 'date', value_column = 'value')
employment_clean
Out[47]:
industry industry_code value cumulative_growth
date
2019Q2 Accommodation and food services 72 1335643.0 0.000000
2019Q2 Accommodation services 721 203289.0 0.000000
2019Q2 Accounting, tax preparation, bookkeeping and p... 5412 112364.0 0.000000
2019Q2 Activities related to credit intermediation 5223 28614.0 0.000000
2019Q2 Activities related to real estate 5313 61290.0 0.000000
... ... ... ... ...
2023Q2 Web search portals, libraries and archives, an... 5192 26583.0 22.282534
2023Q2 Web search portals, libraries, archives, and a... 519 26583.0 22.282534
2023Q2 Wholesale trade 41 820614.0 -0.127911
2023Q2 Wired and wireless telecommunications carriers... 5173 100164.0 -4.625697
2023Q2 Wood product manufacturing 321 94213.0 -1.838963

6341 rows × 4 columns

Which industries have gained the most jobs since 2019Q2?¶

🧐 Let's check which industries have accumulated the highest employment growth since 2019Q2

In [48]:
best_industries_employment = employment_clean.xs(date_range[1]).nlargest(n = 10, columns = 'cumulative_growth')
best_industries_employment
Out[48]:
industry industry_code value cumulative_growth
date
2023Q2 Resin, synthetic rubber, and artificial and sy... 3252 7521.0 68.405732
2023Q2 Support activities for rail transportation 4882 6375.0 64.516129
2023Q2 Computing infrastructure providers, data proce... 518,5182 31807.0 57.538385
2023Q2 Mineral, ore and precious metal merchant whole... 4185 835.0 55.204461
2023Q2 Computer systems design and related services 5415 387582.0 50.284414
2023Q2 Local messengers and local delivery 4922 12111.0 49.334155
2023Q2 Community food and housing, and emergency and ... 6242 23966.0 47.093844
2023Q2 Household appliance manufacturing 3352 2542.0 44.267877
2023Q2 Scientific research and development services 5417 73553.0 41.854545
2023Q2 Railroad rolling stock manufacturing 3365 6157.0 41.280404

🧐🧐 And which one of these industries have also accumulated the highest growth since 2019Q2?

In [49]:
plot_line_graph(df = employment_clean, industry_codes = best_industries_gdp_list, y_col = 'cumulative_growth', title = ''.join(['Employment within industries with the highest cumulative growth since ', date_range[0]]), y_axis_title = 'Cumulative growth (%)')

📈 Nice! We can clearly see, once again, that industries related to computer system designs and related services have had an enourmous growth since 2019!

Salary income analysis¶

👌 So far we have seen that data- and computer-related industries have grown massively since the COVID-19 pandemic.

💰 Have salaries followed suit? Let's take a look!

In [50]:
income_df
Out[50]:
industry industry_code value
date
2001-01-01 Industrial aggregate including unclassified bu... 00-91N NaN
2001-01-01 Industrial aggregate excluding unclassified bu... 11-91N 657.36
2001-01-01 Goods producing industries 11-33N 819.81
2001-01-01 Forestry, logging and support 11N 769.99
2001-01-01 Forestry and logging 113 776.20
... ... ... ...
2023-04-01 Federal government public administration 911 1897.02
2023-04-01 Provincial and territorial public administration 912 1608.10
2023-04-01 Local, municipal and regional public administr... 913 1358.40
2023-04-01 Indigenous public administration 914 987.87
2023-04-01 Unclassified businesses 00 NaN

115416 rows × 3 columns

👇 First let's take a look at the average weekly income across all industries (in CAD dollars):

In [51]:
# Create a line plot using Seaborn
sns.lineplot(data = income_df.query('industry == "Industrial aggregate excluding unclassified businesses"'), x = 'date', y = 'value')

# Highlight a section using axvspan() or axhspan()
plt.axvspan('2020-01-01', '2022-01-01', color='red', alpha=0.2)

# Set labels and title
plt.xlabel('Date')
plt.ylabel('Average weekly income across all industries (CAD $)')

# Show the plot
plt.show()

📈 Interestingly, the average weekly salary across all industries has gone up during the COVID-19 pandemic.

🧐 Which industries have grown their weekly salary the most?

In [52]:
#Calculate the median in each quarter
income_grouped = income_df.groupby([income_df.index.to_period('Q'), 'industry', 'industry_code']).median()
#Filter results between 2019Q2 and 2023Q2
income_clean = filter_datarange(df = income_grouped, date_range = date_range, index_columns = ['industry', 'industry_code'], date_column = 'date', value_column = 'value')
#Gather the top 10 industries with 
best_industries_income = income_clean.xs(date_range[1]).nlargest(n = 10, columns = 'value').sort_values('cumulative_growth', ascending=False)
best_industries_income
Out[52]:
industry industry_code value cumulative_growth
date
2023Q2 Steel product manufacturing from purchased steel 3312 2235.86 65.521173
2023Q2 Alumina and aluminum production and processing 3313 2153.54 52.159229
2023Q2 Non-ferrous metal (except aluminum) production... 3314 2426.11 35.451363
2023Q2 Computing infrastructure providers, data proce... 518,5182 2216.21 26.474348
2023Q2 Software publishers 5132 2269.17 19.054040
2023Q2 Electric power generation, transmission and di... 2211 2197.34 13.580515
2023Q2 Mining, quarrying, and oil and gas extraction 21 2332.81 8.296272
2023Q2 Oil and gas extraction 211,2111 2807.41 3.847377
2023Q2 Mining and quarrying (except oil and gas) 212 2193.31 3.002282
2023Q2 Metal ore mining 2122 2285.84 0.999907
In [53]:
best_industries_income_list = best_industries_income['industry_code'].to_list()
plot_line_graph(df = income_clean, industry_codes = best_industries_income_list, y_col = 'cumulative_growth', title = ''.join(['Industries with the highest cumulative growth in weekly income since ', date_range[0]]), y_axis_title = 'Cumulative growth (%)')

✅ Following a similar trend as before, we see that the "Computing infrastructure providers, data processing, web hosting, and related services" industry is among the top 10 industries with the highest weekly salary gains since 2019Q2.

In [54]:
income_subset_list = ["Computing infrastructure providers, data processing, web hosting, and related services", "Industrial aggregate excluding unclassified businesses"]
income_subset = income_grouped.query('industry in @income_subset_list').reset_index(col_level = 0)
income_subset['date'] = income_subset['date'].astype('str')
sns.relplot(income_subset, x = 'date', y = 'value', kind = 'line', hue = 'industry')
plt.xticks(income_subset[income_subset['date'].str.contains('Q2')]['date'].unique().tolist())
plt.axvline('2020Q2', color='black', linestyle='--')
plt.xticks(rotation=90)
# Set labels and title
plt.xlabel('Date')
plt.ylabel('Average weekly income ($CAD)')
plt.show()

🤓 In fact, we can see the massive jump in average weekly income for "Computing infrastructure providers, data processing, web hosting, and related services" starting in 2020Q2!

Conclusion¶

👉 Different industries have shown distinct growth trajectories in the aftermath of the COVID-19 pandemic.

👉 Online and data-related companies have notably outperformed other sectors since 2020, marking a shift towards digital reliance in the economy.

👉 Traditional sectors such as manufacturing, transport, and leisure bore significant negative impacts from the pandemic in 2020.

👉 Despite their initial robust growth during the pandemic, online and data-centric industries are experiencing a slowdown in their growth rate when compared to the broader industry average.

👉 Conversely, the worst-hit sectors during the pandemic, including manufacturing, transport, and leisure, are still struggling to achieve full recovery.

👉 The computer-related industry has seen a significant surge in job growth since 2020, signifying an increasing demand for tech skills in the economy.

👉 Reflecting a similar trend, the computer and data-related sectors have demonstrated considerable salary growth in recent years, underscoring their rising importance in the economy.